Benefit of Adjuvant Chemotherapy and Pelvic Lymph Node Dissection in pT3 and Node Positive Bladder Cancer Patients Treated with Radical Cystectomy
Bibliographic record
Abstract
Background: Benefits of adjuvant chemotherapy (AC) and extent of pelvic lymph node dissection (PLND) in radical cystectomy (RC) are debated. Results from randomized trials are still expected. Objective: To analyze the effects of AC and PLND in two academic centers with opposite policies regarding their use. Methods: 581 bladder cancer patients who underwent RC without neoadjuvant chemotherapy, from Toronto (University Health Network), Canada, and Turku University Hospital, Finland were included. Disease specific survival (DSS) and failure patterns were assessed. Results: Centers differed in PLND rate (93% and 36% in Toronto and Turku respectively, p < 0.001), PLND extent (≥10 removed nodes, 58% vs. 8%, p < 0.001) and AC rate (21% vs. 2%, p < 0.001). Survival between centers among pT≤1 or pT4 patients was similar. pT3 patients in Toronto had an improved 10 year DSS (43% vs. 22%, p = 0.025). Distant failures were less common after AC (HR 0.56, 95% CI 0.33–0.98, p < 0.042). In node positive (N+) patients, mortality was significantly higher in Turku (HR 2.19, 95% CI 1.44–3.34, p < 0.001) and lower in patients receiving AC (HR 0.60, 95% CI 0.37–0.99, p = 0.044). 41% DSS at 10 years was observed in N+ Toronto patients. Limitations included the non-randomized retrospective design and absence of propensity score analysis. Conclusion: Combining AC and PLND to RC is associated with improved survival in pT3 and N+ patients. PLND did not affect survival independently but helps in selecting patients for AC. Our data adds to the growing body of evidence supporting the usefulness of AC in addition to PLND in high risk patients operated by cystectomy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".